Estimation for the Parameters of Generalized Half-normal Distribution Based on Progressive Type-I Interval Censoring

Based on progressively Type-I interval censored sample, the problem of estimating unknown parameters of a two parameter generalized half-normal(GHN) distribution is considered. Different methods of estimation are discussed. They include the maximum likelihood estimation, midpoint approximation method, approximate maximum likelihood estimation, method of moments, and estimation based on probability plot. Several Bayesian estimates with respect to different symmetric and asymmetric loss functions such as squared error, LINEX, and general entropy is calculated. The Lindley’s approximation method is applied to determine Bayesian estimates. Monte Carlo simulations are performed to compare the performances of the different methods. Finally, analysis is also carried out for a real dataset.     

Lifetime performance index based on ranked set sampling

In this article, we assume that the lifetimes of products follow a one-parameter exponential distribution and use both conjugate and Jeffreys prior distributions for finding a Bayes estimator based on the RSS scheme for lifetime performance index CL. We also obtained maximum likelihood and adhac estimators for CL. Monte Carlo simulation study is done for comparing the obtained estimators in two sampling schemes SRS and RSS. Fisher information has been utilized to obtain lower confidence bound of CL for both schemes in asymptotically state and use this bounds for hypothesis testing of CL.     

A two-step robust estimation of the process mean using M-estimator

Parameter estimation is the first step in constructing control charts. One of these parameters is the process mean. The classical estimators of the process mean are sensitive to the presence of outlying data and subgroups which contaminate the whole data. In existing robust estimators for the process mean, the effects of the presence of the individual outliers are being considered, while, in this paper, a robust estimator is being proposed to reduce the effect of outlying subgroups as well as the individual outliers within a subgroup. The proposed estimator was compared with some classical and robust estimators of the process mean. Although, its relative efficiency is fourth among the estimators tested, its robustness and efficiency are large when the outlying subgroups are present. Evaluation of the results indicated that the proposed estimator is less sensitive to the presence of outliers and the process mean performs well when there are no individual outliers or outlying subgroups.     

Substance Use Disorders in a Sample of Iranian Secondary School Students

Aims: The current research assessed therate of substance use among Iranian secondaryschool students. Participants: 470boy secondary school students selected randomlyand were assessed. Measurements: Aconfidential questionnaire based on DSM-IV anda prior study was distributed, completed by thestudents and collected in the same sessions in2001. Results: Their mean age was13.6 yr., (SD = 0.67) ranging from 12 to 14. Ofthe subjects 105 (22.3%) reported usage ofsubstance (s) once or more sometime duringtheir lives: tobacco 82 (17.5%) alcohol 32(6.8%), opium 3 (–0.6%), hashish 1 (–0.2%), and heroin 2 (0.4%). Only 39 (8.3%) of the students were current substance dependent: 29 (6.2%) were tobacco dependent and 13 (2.8%)were alcohol dependent. Some were using morethan one substance. Conclusions:Tobacco and alcohol were found to be the mostprevalent form of substance use. Seekingpleasure, modeling and release of tension werethe most common reasons for initial substanceuse. The most common reasons reported forcurrent use were seeking pleasure, habit andrelease of tension, respectively. Thesefindings can be implicated in planningpreventive programs. Some of the findings aredifferent from those carried in the west.Cultural attitudes could be the causes of thedifferences.     

Bayesian estimation and prediction for some life distributions based on record values

Some statistical data are most easily accessed in terms of record values. Examples include meteorology, hydrology and athletic events. Also, there are a number of industrial situations where experimental outcomes are a sequence of record-breaking observations. In this paper, Bayesian estimation for the two parameters of some life distributions, including Exponential, Weibull, Pareto and Burr type XII, are obtained based on upper record values. Prediction, either point or interval, for future upper record values is also presented from a Bayesian view point. Some of the non-Bayesian results can be achieved as limiting cases from our results. Numerical computations are given to illustrate the results.     

Prediction of order statistics and record values from two independent sequences

In this paper, we discuss the problem of predicting future order statistics based on observed record values and similarly, the prediction of future records based on observed order statistics. The coverage probabilities of these intervals are exact and are free of the parent distribution F. Finally, two data sets are used to illustrate the proposed procedures.     

Log-gamma-generated families of distributions

We introduce two new general families of continuous distributions, generated by a distribution F and two positive real parameters α and β which control the skewness and tail weight of the distribution. The construction is motivated by the distribution of k-record statistics and can be derived by applying the inverse probability integral transformation to the log-gamma distribution. The introduced families are suitable for modelling the data with a significantly skewed and heavy-tailed distribution. Various properties of the introduced families are studied and a number of estimations and data fitness on real data are given to illustrate the results.     

Efficacy of writing for recovery on traumatic grief symptoms of Afghani refugee bereaved adolescents: a randomized control trial

Effective evidence-based intervention for traumatic bereavement is one of the current major research issues in the field of Post Traumatic Stress Disorder (PTSD) in children and adolescents. The "Writing for Recovery" group intervention is a new treatment approach developed by the Children and War Foundation for traumatized and bereaved children and adolescents after disasters. The purpose of this project was an empirical examination of this intervention with 12- to 18-year-old war bereaved Afghani refugees. Eighty-eight war bereaved Afghani refugees were screened using the Traumatic Grief Inventory for Children (TGIC). From those with the highest total score, 61 were randomly assigned to either an experimental (n = 29) or control group (n = 32). The experimental group received six sessions of group training on 3 consecutive days in their school. The difference of TGIC scores between the experimental group in pretest and posttest was significant (p = 0.001). Results of analysis of covariance also showed a significant effect of Writing for Recovery on the experimental group (p < 0.001). It is concluded that "Writing for Recovery" is an effective group intervention for bereaved children and adolescents after disasters.     

Outer and inner prediction intervals for order statistics intervals based on current records

This paper considers the largest and smallest observations at the times when a new record of either kind (upper or lower) occurs. These are called the upper and lower current records and are denoted by ${R^l_m}$ and ${R^s_m}$ , respectively. The interval ${(R^s_m,R^l_m)}$ is then referred to as the record coverage. The prediction problem in the two-sample case is then discussed and, specifically, the exact outer and inner prediction intervals are derived for order statistics intervals from an independent future Y-sample based on the m-th record coverage from the X-sequence when the underlying distribution of the two samples are the same. The coverage probabilities of these intervals are exact and do not depend on the underlying distribution. Distribution-free prediction intervals as well as upper and lower prediction limits for spacings from a future Y-sample are obtained in terms of the record range from the X-sequence.